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Zoe Szajnfarber and Dava J. Newman. Zhang, Lihui(Lihui Lydia) Massachusetts Institute of Technology. Institute for Data, Systems, and Society. Technology and Policy Program. Massachusetts Institute of Technology. Institute for Data, Systems, and Society Technology and Policy Program Massachusetts Institute of Technology. Engineering Systems Division 2021-05-24T20:24:06Z 2021-05-24T20:24:06Z 2021 2021 https://hdl.handle.net/1721.1/130795 1252064710 Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program, February, 2021 Cataloged from the official PDF of thesis. Includes bibliographical references (pages 104-114). Recent events in the US and beyond have stirred societal debates on how to measure and increase diversity in fields such as Science, Technology, Engineering, and Math (STEM). In the past decade, many US public agencies with STEM missions have explored crowd-sourced, open innovation challenges as a potential solution. To probe whether and how diversity has been achieved in this context, this thesis addresses three goals: (i) understanding the multi-faceted nature of diversity and its determinants based on literature review across disciplines, (ii) testing whether open innovation challenges achieve the policy objective of increasing diversity in solvers for public agencies, and (iii) investigating whether increased diversity leads to desired innovation outcomes. This thesis leverages the NASA-Freelancer Astrobee Challenge Series (ACS), an online robotics design prize competition, as a case study. Through a systematic literature review, this thesis proposes a multi-dimensional diversity definition, covering surface vs. deep, absolute vs. relative, and individual vs. population diversity perspectives. It adopts a mixed approach of inductive coding and natural language processing to exact motivation from solvers; one-way ANOVA and Tukey test to examine the significance between diversity and solution quality generated in ACS. The study finds that ACS, a representative of open innovation challenges, does not generate all dimensions of diversity in its solvers. On the surface-level dimension of diversity, we observe that ACS achieves greater inclusion in some features such as age and country, but not so much on gender. ACS also achieves some extents of deep-level diversity, reflected in features such as education, career backgrounds, and motivation. Furthermore, insights from the study show that achieving diversity of solvers in certain features does not necessarily lead to quality solutions. Based on these insights, this thesis formulates a series of policy implications for STEM federal agencies, exploring the effectiveness of open innovation challenges as a policy mechanism for increasing diversity. It also generates suggestions to improve solution quality of future open innovation challenges. by Lihui (Lydia) Zhang. S.M. in Technology and Policy S.M.inTechnologyandPolicy Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, Technology and Policy Program 114 pages eng Massachusetts Institute of Technology MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided. http://dspace.mit.edu/handle/1721.1/7582 Institute for Data, Systems, and Society. Technology and Policy Program. Crowd equals diversity? a diversity analysis on participation of agency-sponsored open innovation challenges Diversity analysis on participation of agency-sponsored open innovation challenges Thesis application/pdf 2021-05-24T20:24:06Z Publication Master TPP ESD IDSS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="fe69475a-c5c1-49cd-aca0-99683db45943"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Crowd equals diversity? a diversity analysis on participation of agency-sponsored open innovation challenges</Title> <Subtitle>Diversity analysis on participation of agency-sponsored open innovation challenges</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2021</PublicationDate> <Authors> <Author> <DisplayName>Zhang, Lihui(Lihui Lydia)</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Institute for Data, Systems, and Society.</Keyword> <Keyword>Technology and Policy Program.</Keyword> <Abstract>Recent events in the US and beyond have stirred societal debates on how to measure and increase diversity in fields such as Science, Technology, Engineering, and Math (STEM). In the past decade, many US public agencies with STEM missions have explored crowd-sourced, open innovation challenges as a potential solution. To probe whether and how diversity has been achieved in this context, this thesis addresses three goals: (i) understanding the multi-faceted nature of diversity and its determinants based on literature review across disciplines, (ii) testing whether open innovation challenges achieve the policy objective of increasing diversity in solvers for public agencies, and (iii) investigating whether increased diversity leads to desired innovation outcomes. This thesis leverages the NASA-Freelancer Astrobee Challenge Series (ACS), an online robotics design prize competition, as a case study.</Abstract> <Abstract>Through a systematic literature review, this thesis proposes a multi-dimensional diversity definition, covering surface vs. deep, absolute vs. relative, and individual vs. population diversity perspectives. It adopts a mixed approach of inductive coding and natural language processing to exact motivation from solvers; one-way ANOVA and Tukey test to examine the significance between diversity and solution quality generated in ACS. The study finds that ACS, a representative of open innovation challenges, does not generate all dimensions of diversity in its solvers. On the surface-level dimension of diversity, we observe that ACS achieves greater inclusion in some features such as age and country, but not so much on gender. ACS also achieves some extents of deep-level diversity, reflected in features such as education, career backgrounds, and motivation.</Abstract> <Abstract>Furthermore, insights from the study show that achieving diversity of solvers in certain features does not necessarily lead to quality solutions. Based on these insights, this thesis formulates a series of policy implications for STEM federal agencies, exploring the effectiveness of open innovation challenges as a policy mechanism for increasing diversity. It also generates suggestions to improve solution quality of future open innovation challenges.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>